{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capacitance matrix and LOM analysis\n",
    "### Prerequisite\n",
    "You need to have a working local installation of Ansys."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Create the design in Metal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit_metal as metal\n",
    "from qiskit_metal import designs, draw\n",
    "from qiskit_metal import MetalGUI, Dict, Headings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "design = designs.DesignPlanar()\n",
    "gui = MetalGUI(design)\n",
    "\n",
    "from qiskit_metal.qlibrary.qubits.transmon_pocket import TransmonPocket\n",
    "from qiskit_metal.qlibrary.tlines.meandered import RouteMeander"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "design.variables['cpw_width'] = '15 um'\n",
    "design.variables['cpw_gap'] = '9 um'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this example, the design consists of 4 qubits and 4 CPWs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Allow running the same cell here multiple times to overwrite changes\n",
    "design.overwrite_enabled = True\n",
    "\n",
    "## Custom options for all the transmons\n",
    "options = dict(\n",
    "    # Some options we want to modify from the defaults\n",
    "    # (see below for defaults)\n",
    "    pad_width = '425 um', \n",
    "    pocket_height = '650um',\n",
    "    # Adding 4 connectors (see below for defaults)\n",
    "    connection_pads=dict(\n",
    "        readout = dict(loc_W=+1,loc_H=-1, pad_width='200um'),\n",
    "        bus1 = dict(loc_W=-1,loc_H=+1, pad_height='30um'),\n",
    "        bus2 = dict(loc_W=-1,loc_H=-1, pad_height='50um')\n",
    "    )\n",
    ")\n",
    "\n",
    "## Create 4 transmons\n",
    "\n",
    "q1 = TransmonPocket(design, 'Q1', options = dict(\n",
    "    pos_x='+2.42251mm', pos_y='+0.0mm', **options))\n",
    "q2 = TransmonPocket(design, 'Q2', options = dict(\n",
    "    pos_x='+0.0mm', pos_y='-0.95mm', orientation = '270', **options))\n",
    "q3 = TransmonPocket(design, 'Q3', options = dict(\n",
    "    pos_x='-2.42251mm', pos_y='+0.0mm', orientation = '180', **options))\n",
    "q4 = TransmonPocket(design, 'Q4', options = dict(\n",
    "    pos_x='+0.0mm', pos_y='+0.95mm', orientation = '90', **options))\n",
    "\n",
    "RouteMeander.get_template_options(design)\n",
    "\n",
    "options = Dict(\n",
    "        lead=Dict(\n",
    "            start_straight='0.2mm',\n",
    "            end_straight='0.2mm'),\n",
    "        trace_gap='9um',\n",
    "        trace_width='15um')\n",
    "\n",
    "def connect(component_name: str, component1: str, pin1: str, component2: str, pin2: str,\n",
    "            length: str, asymmetry='0 um', flip=False, fillet='90um'):\n",
    "    \"\"\"Connect two pins with a CPW.\"\"\"\n",
    "    myoptions = Dict(\n",
    "        fillet=fillet,\n",
    "        hfss_wire_bonds = True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(\n",
    "                component=component1,\n",
    "                pin=pin1),\n",
    "            end_pin=Dict(\n",
    "                component=component2,\n",
    "                pin=pin2)),\n",
    "        total_length=length)\n",
    "    myoptions.update(options)\n",
    "    myoptions.meander.asymmetry = asymmetry\n",
    "    myoptions.meander.lead_direction_inverted = 'true' if flip else 'false'\n",
    "    return RouteMeander(design, component_name, myoptions)\n",
    "\n",
    "asym = 140\n",
    "cpw1 = connect('cpw1', 'Q1', 'bus2', 'Q2', 'bus1', '6.0 mm', f'+{asym}um')\n",
    "cpw2 = connect('cpw2', 'Q3', 'bus1', 'Q2', 'bus2', '6.1 mm', f'-{asym}um', flip=True)\n",
    "cpw3 = connect('cpw3', 'Q3', 'bus2', 'Q4', 'bus1', '6.0 mm', f'+{asym}um')\n",
    "cpw4 = connect('cpw4', 'Q1', 'bus1', 'Q4', 'bus2', '6.1 mm', f'-{asym}um', flip=True)\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gui.screenshot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Capacitance Analysis and LOM derivation using the analysis package - most users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Capacitance Analysis\n",
    "Select the analysis you intend to run from the `qiskit_metal.analyses` collection.<br>\n",
    "Select the design to analyze and the tool to use for any external simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.analyses.quantization import LOManalysis\n",
    "c1 = LOManalysis(design, \"q3d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(optional) You can review and update the Analysis default setup following the examples in the next two cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Setup',\n",
       " 'reuse_selected_design': True,\n",
       " 'freq_ghz': 5.0,\n",
       " 'save_fields': False,\n",
       " 'enabled': True,\n",
       " 'max_passes': 15,\n",
       " 'min_passes': 2,\n",
       " 'min_converged_passes': 2,\n",
       " 'percent_error': 0.5,\n",
       " 'percent_refinement': 30,\n",
       " 'auto_increase_solution_order': True,\n",
       " 'solution_order': 'High',\n",
       " 'solver_type': 'Iterative'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1.sim.setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Setup',\n",
       " 'reuse_selected_design': True,\n",
       " 'freq_ghz': 5.0,\n",
       " 'save_fields': False,\n",
       " 'enabled': True,\n",
       " 'max_passes': 6,\n",
       " 'min_passes': 2,\n",
       " 'min_converged_passes': 2,\n",
       " 'percent_error': 0.5,\n",
       " 'percent_refinement': 30,\n",
       " 'auto_increase_solution_order': 'False',\n",
       " 'solution_order': 'Medium',\n",
       " 'solver_type': 'Iterative'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example: update single setting\n",
    "c1.sim.setup.max_passes = 6\n",
    "# example: update multiple settings\n",
    "c1.sim.setup_update(solution_order = 'Medium', auto_increase_solution_order = 'False')\n",
    "\n",
    "c1.sim.setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyze a single qubit with 2 endcaps using the default (or edited) analysis setup. Then show the capacitance matrix (from the last pass).\n",
    "\n",
    "You can use the method `run()` instead of `sim.run()` in the following cell if you want to run both cap extraction and lom analysis in a single step. If so, make sure to also tweak the setup for the lom analysis. The input parameters are otherwise the same for the two methods. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 08:32AM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 08:32AM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 08:32AM [load_ansys_project]: \tOpened Ansys Desktop v2020.2.0\n",
      "INFO 08:32AM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Ansoft/\n",
      "\tProject:   Project22\n",
      "INFO 08:32AM [connect_design]: No active design found (or error getting active design).\n",
      "INFO 08:32AM [connect]: \t Connected to project \"Project22\". No design detected\n",
      "INFO 08:32AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Design_q3d [Solution type: Q3D]\n",
      "WARNING 08:32AM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 08:32AM [connect_setup]: \tCreating Q3D default setup.\n",
      "INFO 08:32AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 08:32AM [get_setup]: \tOpened setup `Setup1`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 08:32AM [analyze]: Analyzing setup Setup1\n",
      "INFO 08:33AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp9kdob592.txt, C, , Setup1:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 1, False\n",
      "INFO 08:33AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpsk8_aon5.txt, C, , Setup1:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 1, False\n",
      "INFO 08:33AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp6zdkc4y0.txt, C, , Setup1:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 2, False\n",
      "INFO 08:33AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp2hpfznmn.txt, C, , Setup1:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 3, False\n",
      "INFO 08:33AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpj38u4y4a.txt, C, , Setup1:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 4, False\n",
      "INFO 08:33AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpknqz7sw0.txt, C, , Setup1:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 5, False\n",
      "INFO 08:33AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp9mj4v6i8.txt, C, , Setup1:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 6, False\n",
      "INFO 08:33AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpj0mu6o1u.txt, C, , Setup1:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 7, False\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bus1_connector_pad_Q1</th>\n",
       "      <th>bus2_connector_pad_Q1</th>\n",
       "      <th>ground_main_plane</th>\n",
       "      <th>pad_bot_Q1</th>\n",
       "      <th>pad_top_Q1</th>\n",
       "      <th>readout_connector_pad_Q1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bus1_connector_pad_Q1</th>\n",
       "      <td>49.77794</td>\n",
       "      <td>-0.42560</td>\n",
       "      <td>-33.50861</td>\n",
       "      <td>-1.57029</td>\n",
       "      <td>-13.15538</td>\n",
       "      <td>-0.20494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bus2_connector_pad_Q1</th>\n",
       "      <td>-0.42560</td>\n",
       "      <td>54.01885</td>\n",
       "      <td>-35.77522</td>\n",
       "      <td>-13.99741</td>\n",
       "      <td>-1.82852</td>\n",
       "      <td>-1.01319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ground_main_plane</th>\n",
       "      <td>-33.50861</td>\n",
       "      <td>-35.77522</td>\n",
       "      <td>237.69029</td>\n",
       "      <td>-31.53485</td>\n",
       "      <td>-37.88741</td>\n",
       "      <td>-36.55732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pad_bot_Q1</th>\n",
       "      <td>-1.57029</td>\n",
       "      <td>-13.99741</td>\n",
       "      <td>-31.53485</td>\n",
       "      <td>98.20667</td>\n",
       "      <td>-30.07382</td>\n",
       "      <td>-18.86490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pad_top_Q1</th>\n",
       "      <td>-13.15538</td>\n",
       "      <td>-1.82852</td>\n",
       "      <td>-37.88741</td>\n",
       "      <td>-30.07382</td>\n",
       "      <td>87.85076</td>\n",
       "      <td>-2.20122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>readout_connector_pad_Q1</th>\n",
       "      <td>-0.20494</td>\n",
       "      <td>-1.01319</td>\n",
       "      <td>-36.55732</td>\n",
       "      <td>-18.86490</td>\n",
       "      <td>-2.20122</td>\n",
       "      <td>59.92347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          bus1_connector_pad_Q1  bus2_connector_pad_Q1  \\\n",
       "bus1_connector_pad_Q1                  49.77794               -0.42560   \n",
       "bus2_connector_pad_Q1                  -0.42560               54.01885   \n",
       "ground_main_plane                     -33.50861              -35.77522   \n",
       "pad_bot_Q1                             -1.57029              -13.99741   \n",
       "pad_top_Q1                            -13.15538               -1.82852   \n",
       "readout_connector_pad_Q1               -0.20494               -1.01319   \n",
       "\n",
       "                          ground_main_plane  pad_bot_Q1  pad_top_Q1  \\\n",
       "bus1_connector_pad_Q1             -33.50861    -1.57029   -13.15538   \n",
       "bus2_connector_pad_Q1             -35.77522   -13.99741    -1.82852   \n",
       "ground_main_plane                 237.69029   -31.53485   -37.88741   \n",
       "pad_bot_Q1                        -31.53485    98.20667   -30.07382   \n",
       "pad_top_Q1                        -37.88741   -30.07382    87.85076   \n",
       "readout_connector_pad_Q1          -36.55732   -18.86490    -2.20122   \n",
       "\n",
       "                          readout_connector_pad_Q1  \n",
       "bus1_connector_pad_Q1                     -0.20494  \n",
       "bus2_connector_pad_Q1                     -1.01319  \n",
       "ground_main_plane                        -36.55732  \n",
       "pad_bot_Q1                               -18.86490  \n",
       "pad_top_Q1                                -2.20122  \n",
       "readout_connector_pad_Q1                  59.92347  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1.sim.run(components=['Q1'], open_terminations=[('Q1', 'readout'), ('Q1', 'bus1'), ('Q1', 'bus2')])\n",
    "c1.sim.capacitance_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last variables you pass to the `run()` or `sim.run()` methods, will be stored in the `sim.setup` dictionary under the key `run`. You can recall the information passed by either accessing the dictionary directly, or by using the print handle below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This analysis object run with the following kwargs:\n",
      "{}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# c1.setup.run    <- direct access\n",
    "c1.print_run_args()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can re-run the analysis after varying the parameters.<br>\n",
    "Not passing the parameter `components` to the `sim.run()` method, skips the rendering and tries to run the analysis on the latest design. If a design is not found, the full metal design is rendered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 08:33AM [get_setup]: \tOpened setup `Setup2`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 08:33AM [analyze]: Analyzing setup Setup2\n",
      "INFO 08:34AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp2zqpsp70.txt, C, , Setup2:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 4800000000, Maxwell, 1, False\n",
      "INFO 08:34AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp5r0te98b.txt, C, , Setup2:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 4800000000, Maxwell, 1, False\n",
      "INFO 08:34AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpvakd3cxo.txt, C, , Setup2:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 4800000000, Maxwell, 2, False\n",
      "INFO 08:34AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmppo2b9z67.txt, C, , Setup2:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 4800000000, Maxwell, 3, False\n",
      "INFO 08:34AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpskxpuv4a.txt, C, , Setup2:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 4800000000, Maxwell, 4, False\n",
      "INFO 08:34AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmprf7et9ty.txt, C, , Setup2:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 4800000000, Maxwell, 5, False\n",
      "INFO 08:34AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpuooj6mi9.txt, C, , Setup2:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 4800000000, Maxwell, 6, False\n",
      "INFO 08:34AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpm9a1sk65.txt, C, , Setup2:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 4800000000, Maxwell, 7, False\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bus1_connector_pad_Q1</th>\n",
       "      <th>bus2_connector_pad_Q1</th>\n",
       "      <th>ground_main_plane</th>\n",
       "      <th>pad_bot_Q1</th>\n",
       "      <th>pad_top_Q1</th>\n",
       "      <th>readout_connector_pad_Q1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bus1_connector_pad_Q1</th>\n",
       "      <td>49.77794</td>\n",
       "      <td>-0.42560</td>\n",
       "      <td>-33.50861</td>\n",
       "      <td>-1.57029</td>\n",
       "      <td>-13.15538</td>\n",
       "      <td>-0.20494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bus2_connector_pad_Q1</th>\n",
       "      <td>-0.42560</td>\n",
       "      <td>54.01885</td>\n",
       "      <td>-35.77522</td>\n",
       "      <td>-13.99741</td>\n",
       "      <td>-1.82852</td>\n",
       "      <td>-1.01319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ground_main_plane</th>\n",
       "      <td>-33.50861</td>\n",
       "      <td>-35.77522</td>\n",
       "      <td>237.69029</td>\n",
       "      <td>-31.53485</td>\n",
       "      <td>-37.88741</td>\n",
       "      <td>-36.55732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pad_bot_Q1</th>\n",
       "      <td>-1.57029</td>\n",
       "      <td>-13.99741</td>\n",
       "      <td>-31.53485</td>\n",
       "      <td>98.20667</td>\n",
       "      <td>-30.07382</td>\n",
       "      <td>-18.86490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pad_top_Q1</th>\n",
       "      <td>-13.15538</td>\n",
       "      <td>-1.82852</td>\n",
       "      <td>-37.88741</td>\n",
       "      <td>-30.07382</td>\n",
       "      <td>87.85076</td>\n",
       "      <td>-2.20122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>readout_connector_pad_Q1</th>\n",
       "      <td>-0.20494</td>\n",
       "      <td>-1.01319</td>\n",
       "      <td>-36.55732</td>\n",
       "      <td>-18.86490</td>\n",
       "      <td>-2.20122</td>\n",
       "      <td>59.92347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          bus1_connector_pad_Q1  bus2_connector_pad_Q1  \\\n",
       "bus1_connector_pad_Q1                  49.77794               -0.42560   \n",
       "bus2_connector_pad_Q1                  -0.42560               54.01885   \n",
       "ground_main_plane                     -33.50861              -35.77522   \n",
       "pad_bot_Q1                             -1.57029              -13.99741   \n",
       "pad_top_Q1                            -13.15538               -1.82852   \n",
       "readout_connector_pad_Q1               -0.20494               -1.01319   \n",
       "\n",
       "                          ground_main_plane  pad_bot_Q1  pad_top_Q1  \\\n",
       "bus1_connector_pad_Q1             -33.50861    -1.57029   -13.15538   \n",
       "bus2_connector_pad_Q1             -35.77522   -13.99741    -1.82852   \n",
       "ground_main_plane                 237.69029   -31.53485   -37.88741   \n",
       "pad_bot_Q1                        -31.53485    98.20667   -30.07382   \n",
       "pad_top_Q1                        -37.88741   -30.07382    87.85076   \n",
       "readout_connector_pad_Q1          -36.55732   -18.86490    -2.20122   \n",
       "\n",
       "                          readout_connector_pad_Q1  \n",
       "bus1_connector_pad_Q1                     -0.20494  \n",
       "bus2_connector_pad_Q1                     -1.01319  \n",
       "ground_main_plane                        -36.55732  \n",
       "pad_bot_Q1                               -18.86490  \n",
       "pad_top_Q1                                -2.20122  \n",
       "readout_connector_pad_Q1                  59.92347  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1.sim.setup.freq_ghz = 4.8\n",
    "c1.sim.run()\n",
    "c1.sim.capacitance_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(c1.sim.capacitance_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lumped oscillator model (LOM)\n",
    "\n",
    "Using capacitance matrices obtained from each pass, save the many parameters of the Hamiltonian of the system. `get_lumped_oscillator()` operates on 4 setup parameters: <br><br>\n",
    "Lj: float <br>\n",
    "Cj: float <br>\n",
    "fr: Union[list, float] <br>\n",
    "fb: Union[list, float] <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 4] [5 0 1]\n",
      "Predicted Values\n",
      "\n",
      "Transmon Properties\n",
      "f_Q 5.424935 [GHz]\n",
      "EC 311.976959 [MHz]\n",
      "EJ 13.273404 [GHz]\n",
      "alpha -363.792421 [MHz]\n",
      "dispersion 46.550369 [KHz]\n",
      "Lq 12.305036 [nH]\n",
      "Cq 62.088650 [fF]\n",
      "T1 35.336766 [us]\n",
      "\n",
      "**Coupling Properties**\n",
      "\n",
      "tCqbus1 7.383750 [fF]\n",
      "gbus1_in_MHz 114.265793 [MHz]\n",
      "χ_bus1 -3.174191 [MHz]\n",
      "1/T1bus1 2809.277762 [Hz]\n",
      "T1bus1 56.653331 [us]\n",
      "\n",
      "tCqbus2 -6.455281 [fF]\n",
      "gbus2_in_MHz -85.831803 [MHz]\n",
      "χ_bus2 -9.970470 [MHz]\n",
      "1/T1bus2 1205.469467 [Hz]\n",
      "T1bus2 132.027353 [us]\n",
      "\n",
      "tCqbus3 5.372192 [fF]\n",
      "gbus3_in_MHz 73.765178 [MHz]\n",
      "χ_bus3 -4.515417 [MHz]\n",
      "1/T1bus3 489.200415 [Hz]\n",
      "T1bus3 325.336893 [us]\n",
      "Bus-Bus Couplings\n",
      "gbus1_2 7.097123 [MHz]\n",
      "gbus1_3 9.957495 [MHz]\n",
      "gbus2_3 5.377146 [MHz]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fQ</th>\n",
       "      <th>EC</th>\n",
       "      <th>EJ</th>\n",
       "      <th>alpha</th>\n",
       "      <th>dispersion</th>\n",
       "      <th>gbus</th>\n",
       "      <th>chi_in_MHz</th>\n",
       "      <th>χr MHz</th>\n",
       "      <th>gr MHz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.748104</td>\n",
       "      <td>353.230672</td>\n",
       "      <td>13.273404</td>\n",
       "      <td>-417.375147</td>\n",
       "      <td>135.208268</td>\n",
       "      <td>[108.79200978040816, -73.21708181328188, 76.45...</td>\n",
       "      <td>[-4.790610087146514, -26.5770698752867, -12.45...</td>\n",
       "      <td>4.790610</td>\n",
       "      <td>108.792010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.664672</td>\n",
       "      <td>342.295447</td>\n",
       "      <td>13.273404</td>\n",
       "      <td>-403.045739</td>\n",
       "      <td>103.898327</td>\n",
       "      <td>[112.48401834623373, -82.16156406673579, 68.80...</td>\n",
       "      <td>[-4.4594265208402275, -22.018835553935766, -7....</td>\n",
       "      <td>4.459427</td>\n",
       "      <td>112.484018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.574019</td>\n",
       "      <td>330.639537</td>\n",
       "      <td>13.273404</td>\n",
       "      <td>-387.872732</td>\n",
       "      <td>77.330688</td>\n",
       "      <td>[111.33612864790418, -84.09226785308748, 71.77...</td>\n",
       "      <td>[-3.7804661114555205, -15.865600375824991, -6....</td>\n",
       "      <td>3.780466</td>\n",
       "      <td>111.336129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.523032</td>\n",
       "      <td>324.186357</td>\n",
       "      <td>13.273404</td>\n",
       "      <td>-379.516629</td>\n",
       "      <td>65.212167</td>\n",
       "      <td>[110.99790102064628, -84.10822360591214, 72.87...</td>\n",
       "      <td>[-3.4720716615303537, -13.185856349486748, -5....</td>\n",
       "      <td>3.472072</td>\n",
       "      <td>110.997901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.463772</td>\n",
       "      <td>316.778339</td>\n",
       "      <td>13.273404</td>\n",
       "      <td>-369.962716</td>\n",
       "      <td>53.275261</td>\n",
       "      <td>[113.07905190117796, -84.87724687606234, 72.79...</td>\n",
       "      <td>[-3.293717894242756, -11.011779798984726, -4.8...</td>\n",
       "      <td>3.293718</td>\n",
       "      <td>113.079052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.424935</td>\n",
       "      <td>311.976959</td>\n",
       "      <td>13.273404</td>\n",
       "      <td>-363.792421</td>\n",
       "      <td>46.550369</td>\n",
       "      <td>[114.26579286281127, -85.83180315154853, 73.76...</td>\n",
       "      <td>[-3.1741912468040616, -9.970469520050226, -4.5...</td>\n",
       "      <td>3.174191</td>\n",
       "      <td>114.265793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fQ          EC         EJ       alpha  dispersion  \\\n",
       "1  5.748104  353.230672  13.273404 -417.375147  135.208268   \n",
       "2  5.664672  342.295447  13.273404 -403.045739  103.898327   \n",
       "3  5.574019  330.639537  13.273404 -387.872732   77.330688   \n",
       "4  5.523032  324.186357  13.273404 -379.516629   65.212167   \n",
       "5  5.463772  316.778339  13.273404 -369.962716   53.275261   \n",
       "6  5.424935  311.976959  13.273404 -363.792421   46.550369   \n",
       "\n",
       "                                                gbus  \\\n",
       "1  [108.79200978040816, -73.21708181328188, 76.45...   \n",
       "2  [112.48401834623373, -82.16156406673579, 68.80...   \n",
       "3  [111.33612864790418, -84.09226785308748, 71.77...   \n",
       "4  [110.99790102064628, -84.10822360591214, 72.87...   \n",
       "5  [113.07905190117796, -84.87724687606234, 72.79...   \n",
       "6  [114.26579286281127, -85.83180315154853, 73.76...   \n",
       "\n",
       "                                          chi_in_MHz    χr MHz      gr MHz  \n",
       "1  [-4.790610087146514, -26.5770698752867, -12.45...  4.790610  108.792010  \n",
       "2  [-4.4594265208402275, -22.018835553935766, -7....  4.459427  112.484018  \n",
       "3  [-3.7804661114555205, -15.865600375824991, -6....  3.780466  111.336129  \n",
       "4  [-3.4720716615303537, -13.185856349486748, -5....  3.472072  110.997901  \n",
       "5  [-3.293717894242756, -11.011779798984726, -4.8...  3.293718  113.079052  \n",
       "6  [-3.1741912468040616, -9.970469520050226, -4.5...  3.174191  114.265793  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1.setup.junctions = Dict({'Lj': 12.31, 'Cj': 2})\n",
    "c1.setup.freq_readout = 7.0\n",
    "c1.setup.freq_bus = [6.0, 6.2]\n",
    "\n",
    "c1.run_lom()\n",
    "c1.lumped_oscillator_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Design \"Design_q3d\" info:\n",
      "\t# eigenmodes    0\n",
      "\t# variations    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 08:34AM [hfss_report_full_convergence]: Creating report for variation 0\n"
     ]
    }
   ],
   "source": [
    "c1.plot_convergence();\n",
    "c1.plot_convergence_chi()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you are done with your analysis, please close it with `close()`. This will free up resources currently occupied by qiskit-metal to communiate with the tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1.sim.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Directly access the renderer to modify other parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 08:34AM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 08:34AM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 08:34AM [load_ansys_project]: \tOpened Ansys Desktop v2020.2.0\n",
      "INFO 08:34AM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Ansoft/\n",
      "\tProject:   Project22\n",
      "INFO 08:34AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Design_q3d [Solution type: Q3D]\n",
      "INFO 08:34AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 08:34AM [connect]: \tConnected to project \"Project22\" and design \"Design_q3d\" 😀 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<qiskit_metal.renderers.renderer_ansys.q3d_renderer.QQ3DRenderer at 0x268898f7fc8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1.sim.start()\n",
    "c1.sim.renderer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every renderer will have its own collection of methods. Below an example with q3d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Prepare and run a collection of predefined setups\n",
    "\n",
    "This is equivalent to going to the Project Manager panel in Ansys, right clicking on Analysis within the active Q3D design, selecting \"Add Solution Setup...\", and choosing/entering default values in the resulting popup window. You might want to do this to keep track of different solution setups, giving each of them a different/specific name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "setup = c1.sim.renderer.new_ansys_setup(name = \"Setup_demo\", max_passes = 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can directly pass to `new_ansys_setup` all the setup parameters. Of course you will then need to run the individual setups by name as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 08:34AM [get_setup]: \tOpened setup `Setup_demo`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 08:34AM [analyze]: Analyzing setup Setup_demo\n"
     ]
    }
   ],
   "source": [
    "c1.sim.renderer.analyze_setup(setup.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Get the capactiance matrix at a different pass\n",
    "\n",
    "You might want to use this if you intend to know what was the matrix at a different pass of the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 08:34AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp3caa8vjk.txt, C, , Setup_demo:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 5, False\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(                          bus1_connector_pad_Q1  bus2_connector_pad_Q1  \\\n",
       " bus1_connector_pad_Q1                  48.92803               -0.42235   \n",
       " bus2_connector_pad_Q1                  -0.42235               53.06961   \n",
       " ground_main_plane                     -33.01607              -35.14374   \n",
       " pad_bot_Q1                             -1.53861              -13.67515   \n",
       " pad_top_Q1                            -12.84517               -1.81368   \n",
       " readout_connector_pad_Q1               -0.20677               -0.99928   \n",
       " \n",
       "                           ground_main_plane  pad_bot_Q1  pad_top_Q1  \\\n",
       " bus1_connector_pad_Q1             -33.01607    -1.53861   -12.84517   \n",
       " bus2_connector_pad_Q1             -35.14374   -13.67515    -1.81368   \n",
       " ground_main_plane                 234.01567   -31.50558   -37.64437   \n",
       " pad_bot_Q1                        -31.50558    96.95568   -29.44070   \n",
       " pad_top_Q1                        -37.64437   -29.44070    86.62922   \n",
       " readout_connector_pad_Q1          -36.15710   -18.48102    -2.19686   \n",
       " \n",
       "                           readout_connector_pad_Q1  \n",
       " bus1_connector_pad_Q1                     -0.20677  \n",
       " bus2_connector_pad_Q1                     -0.99928  \n",
       " ground_main_plane                        -36.15710  \n",
       " pad_bot_Q1                               -18.48102  \n",
       " pad_top_Q1                                -2.19686  \n",
       " readout_connector_pad_Q1                  59.14496  ,\n",
       " 'fF')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the analysis results, get its capacitance matrix as a dataframe.\n",
    "c1.sim.renderer.get_capacitance_matrix(variation = '', solution_kind = 'AdaptivePass', pass_number = 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code to swap rows and columns in capacitance matrix\n",
    "from qiskit_metal.analyses.quantization.lumped_capacitive import df_reorder_matrix_basis\n",
    "\n",
    "df_reorder_matrix_basis(fourq_q3d.get_capacitance_matrix(), 1, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Close the renderer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1.sim.close()"
   ]
  }
 ],
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